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2015
We often assume that Gaussian processes are isotropic implying that the covariance function only depends on the distance between locations.
Yunfei Xu +3 more
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We often assume that Gaussian processes are isotropic implying that the covariance function only depends on the distance between locations.
Yunfei Xu +3 more
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Fuzzy function learning with covariance ellipsoids
IEEE International Conference on Neural Networks, 2002It is shown how first- and second-order statistics can estimate fuzzy rules and sets from input-output data. The fuzzy system approximates the function by covering its graph with fuzzy patches in the input-output state space. The neural quantizer system uses unsupervised competitive learning to estimate the local centroids and covariances of pattern ...
Julie A. Dickerson, Bart Kosko
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Variogram and Covariance Function
1995The experimental variogram is a convenient tool for the analysis of spatial data as it is based on a simple measure of dissimilarity. Its theoretical counterpart reveals that a broad class of phenomena are adequately described by it, including phenomena of unbounded variation.
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Examples of Covariance Functions
1995We present a few models of covariance functions. They are defined for isotropic (i.e. rotation invariant) random functions. On the graphical representations the covariance functions are plotted as variograms using the relation γ(h) = C(0) - C(h).
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Generalized Covariance Functions and Their Applications in Estimation
manuscripta geodaetica, 1984zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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1995
It is actually difficult to characterize directly a covariance function matrix. This becomes easy in the spectral domain on the basis of Cramer’s generalization of the Bochner theorem, which is presented in this chapter. We consider complex covariance functions.
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It is actually difficult to characterize directly a covariance function matrix. This becomes easy in the spectral domain on the basis of Cramer’s generalization of the Bochner theorem, which is presented in this chapter. We consider complex covariance functions.
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ON THE CHOICE OF THE LOSS FUNCTION IN COVARIANCE ESTIMATION
Statistics & Risk Modeling, 1990Summary: In a generalized linear model, under certain conditions, the covariance matrices of a two-stage Aitken estimator and the Gauss-Markov estimator are related via Kariya's inequality of the form \[ Cov({\hat \beta}(\Omega))\leq Cov({\hat \beta}({\hat \Omega}))\leq \epsilon_{\gamma}[{\mathfrak L}(\gamma,{\hat \gamma})]Cov({\hat \beta}(\Omega)), \]
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A class of covariate-dependent spatiotemporal covariance functions.
The annals of applied statisticsIn geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure.
Brian J, Reich +4 more
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Covariant Hyperelliptic Functions of Genus Two
Theoretical and Mathematical Physics, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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